One challenge that holds the field back is the noise that plagues quantum machines. This leads to higher error rates.

The building blocks of a quantum computer are noisy because of interference from the environment, imperfect control signals, and unwanted interactions between qubits. A quantum circuit is a series of operations called quantum gates. These quantum gates, which are mapped to the individual qubits, change the quantum states of certain qubits, which then perform the calculations to solve a problem.

The noise from quantum gates can affect a quantum machine.

Researchers at MIT are working on a way to make the quantum circuit resistant to noise. These quantum circuits areparameterized and have variable quantum gates. The framework created by the team can identify the most robust quantum circuit for a particular computing task and generate a mapping pattern that is tailored to the qubits of a targeted quantum device.

Their framework, called QuantumNAS, is less intensive than other search methods and can identify quantum circuits that improve the accuracy of machine learning and quantum chemistry tasks. When the researchers used their technique to identify quantum circuits for real quantum devices, their circuits were better than those generated using other methods.

We have to sample each individual quantum circuit architecture and mapping scenario in the design space, train them, evaluate them, and if it is not good, throw it away and start over. Song Han, an associate professor in the Department of Electrical Engineering and Computer Science, is the senior author of the paper.

Hanrui Wang and Yujun Lin are both EECS graduate students, as well as Yongshan Ding, an assistant professor of computer science at Yale University, and David Z. Pan, the Silicon Laboratories Endowed Chair in Electrical Engineering at the University of Texas at Austin. The research will be presented at a conference.

**There are many design choices.**

Selecting a number of quantum gates is a part of constructing a quantum circuit. There are many different types of gates to choose from. A circuit can have any number of gates, and the positions of those gates can vary.

The design space is large because of so many different choices. How to design a good circuit architecture is the challenge. Wang says that they want to design that architecture so it can be very robust to noise.

The researchers focused on variational quantum circuits, which use quantum gates with trainable parameters that can learn a machine learning or quantum chemistry task. To design a variational quantum circuit, a researcher must either hand-design the circuit or use rule-based methods to design the circuit for a particular task, and then try to find the ideal set of parameters for each quantum gate through an optimization process.

The parameters for each candidate quantum circuit must be trained in order for the search method to work. The ideal number of parameters and the circuit architecture must be identified by the researcher.

Neural networks that include more parameters can increase the model accuracy. More parameters require more quantum gates, which introduce more noise.

The researchers want to reduce the overall search and training cost while identifying the quantum circuit that contains the ideal number of parameters and appropriate architecture to maximize accuracy and minimize noise.

**Building a circuit.**

The first thing they have to do is design a SuperCircuit, which contains all the possible quantum gates. Smaller quantum circuits can be tested using that SuperCircuit.

They train the SuperCircuit once, and then all other candidate circuits in the design space inherit corresponding parameters that have already been trained. The process has an overhead.

They use the SuperCircuit to search for circuit architectures that meet a targeted objective, in this case high robustness to noise. The process involves searching for both quantum circuits and qubit mappings at the same time.

A noise model or a real machine is used to evaluate the accuracy of the quantum circuit and qubit mapping candidates. The best performing parts are fed back to the algorithm, which uses them to start the process again until it finds the ideal candidates.

Different qubits have different properties and gate error rates. Since we are only using a subset of the qubits, why not use the most reliable ones? Wang says that we can do this through co-search of the architecture and qubit mapping.

Once the researchers have arrived at the best quantum circuit, they train its parameters and perform quantum gate pruning by removing any quantum gates that have values close to zero. The removal of these gates improves the performance of real quantum machines. They fine-tune the remaining parameters to recover accuracy that was lost.

They can deploy the quantum circuit to a real machine after that step.

The researchers were able to beat all the baselines when they tested their circuits on real quantum devices. In one experiment, they used QuantumNAS to create a noise-robust quantum circuit that was used to estimate the ground state energy for a particular molecule, which is an important step in quantum chemistry and drug discovery. Their method was more accurate than any of the baselines.

They want to use these principles to make the parameters in a quantum circuit robust to noise now that they have shown the effectiveness of QuantumNAS. The researchers want to improve the scalability of a quantum neural network by training a quantum circuit on a real quantum machine.

Yiyu Shi, a professor of computer science and engineering at the University of Notre Dame, was not involved with this research.

In this work, Hanrui and her team alleviate the challenge of searching for an efficient parametrized quantum circuit by training one SuperCircuit and using it to evaluate many candidates which becomes very efficient as it requires one training procedure. The SuperCircuit can be used to find circuit ansatz and qubit mapping. We can use the SuperCircuit to find the circuit ansatz and qubit mapping. The protocol has been tested using IBM Q quantum machines.

To encourage more work in this area, the researchers created an open-source library, called TorchQuantum, that contains information about their projects, tutorials, and tools that can be used by other research groups.